This paper attempts to probabilistically estimate the surface soil moisture content (SMC) by using the synthetic aperture radar data provided by radar imaging satellite1. The novelty of this paper lies in: 1) developing a combined index to understand the role of all the backscattering coefficients with different polarization and soil texture information in influencing the SMC; 2) using normalized incidence angles, which enables the model to be applicable for different incidence angles; and 3) determination of uncertainty range of the estimated SMC. The dimensionality problem, which is frequently observed in the multivariate analysis, is reduced in the development of the combined index by the use of supervised principal component analysis (SPCA). The SPCA also ensures the maximum attainable association between the developed combined index and surface SMC above wilting point (WP). The association between the combined index and the surface SMC above WP is modeled through joint probability distribution by using the Frank copula. The model is developed and validated with the field soil moisture values over 334 monitoring points within the study area. The outcomes obtained by applying the proposed model indicate an encouraging potential of the model to be applied for bareland and vegetated land (<30 cm height). © 1980-2012 IEEE.